Energy-Aware Learning Agent (EALA) for Disaggregated Cloud Scheduling

Abstract

Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.